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Diffusion probabilistic models (DPMs) are a class of powerful deep generative models (DGMs).
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2010
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2011
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
Earlier work this paper cites.
A note on the evaluation of generative models
Theis, L., Oord, A. v. d., and Bethge, M · 2015
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Zhang, Y., Song, S., Seff, A., and Xiao, J · 2015
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
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Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2018
Earlier work this paper cites.
Efficient neural audio synthesis
Kalchbrenner, N., Elsen, E., Simonyan, K., Noury, S., Casagrande, N., Lockhart, E., Stimberg, F., Oord, A., Dieleman, S., and Kavukcuoglu, K · 2018
Earlier work this paper cites.
High fidelity speech synthesis with adversarial networks
Bińkowski, M., Donahue, J., Dieleman, S., Clark, A., Elsen, E., Casagrande, N., Cobo, L. C., and Simonyan, K · 2019
Earlier work this paper cites.
Logan: Latent optimisation for generative adversarial networks
Wu, Y., Donahue, J., Balduzzi, D., Simonyan, K., and Lillicrap, T · 2019
Cited alongside, same era.
Wavegrad: Estimating gradients for waveform generation
Chen, N., Zhang, Y., Zen, H., Weiss, R. J., Norouzi, M., and Chan, W · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Analyzing and improving the image quality of stylegan
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T · 2020
Cited alongside, same era.
Diffwave: A versatile diffusion model for audio synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B · 2020
Improved denoising diffusion probabilistic models
Nichol, A. and Dhariwal, P · 2021
Later among the works it cites.
Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., and Chen, M · 2021
Later among the works it cites.
Diffusion-based voice conversion with fast maximum likelihood sampling scheme
Popov, V., Vovk, I., Gogoryan, V., Sadekova, T., Kudinov, M., and Wei, J · 2021
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Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting
Rasul, K., Seward, C., Schuster, I., and Vollgraf, R · 2021
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Cited alongside, same era.
Ilvr: Conditioning method for denoising diffusion probabilistic models
Choi, J., Kim, S., Jeong, Y., Gwon, Y., and Yoon, S · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
Cited alongside, same era.
Score-based generative modeling with critically-damped langevin diffusion
Dockhorn, T., Vahdat, A., and Kreis, K · 2021
Cited alongside, same era.
Gotta go fast when generating data with score-based models
Jolicoeur-Martineau, A., Li, K., Piché-Taillefer, R., Kachman, T., and Mitliagkas, I · 2021
Cited alongside, same era.
Kingma, D. P., Salimans, T., Poole, B., and Ho, J · 2021
Cited alongside, same era.
Srdiff: Single image super-resolution with diffusion probabilistic models
Li, H., Yang, Y., Chang, M., Feng, H., Xu, Z., Li, Q., and Chen, Y · 2021
Cited alongside, same era.
Knowledge distillation in iterative generative models for improved sampling speed
Luhman, E. and Luhman, T · 2021
Cited alongside, same era.
Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D. J., and Norouzi, M · 2021
Later among the works it cites.
Unit-ddpm: Unpaired image translation with denoising diffusion probabilistic models
Sasaki, H., Willcocks, C. G., and Breckon, T. P · 2021
Later among the works it cites.
D2c: Diffusion-denoising models for few-shot conditional generation
Sinha, A., Song, J., Meng, C., and Ermon, S · 2021
Later among the works it cites.
Maximum likelihood training of score-based diffusion models
Song, Y., Durkan, C., Murray, I., and Ermon, S · 2021
Later among the works it cites.
Score-based generative modeling in latent space
Vahdat, A., Kreis, K., and Kautz, J · 2021
Later among the works it cites.
Learning to efficiently sample from diffusion probabilistic models
Watson, D., Ho, J., Norouzi, M., and Chan, W · 2021
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Tackling the generative learning trilemma with denoising diffusion gans
Xiao, Z., Kreis, K., and Vahdat, A · 2021
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3d shape generation and completion through point-voxel diffusion
Zhou, L., Du, Y., and Wu, J · 2021
Later among the works it cites.
Analytic-dpm: an analytic estimate of the optimal reverse variance in diffusion probabilistic models
Bao, F., Li, C., Zhu, J., and Zhang, B · 2022
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